Overview

Dataset statistics

Number of variables16
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)1.0%
Total size in memory25.8 KiB
Average record size in memory128.6 B

Variable types

Numeric16

Alerts

Dataset has 2 (1.0%) duplicate rowsDuplicates
bore is highly overall correlated with city_mpg and 8 other fieldsHigh correlation
city_mpg is highly overall correlated with bore and 8 other fieldsHigh correlation
curb_weight is highly overall correlated with bore and 9 other fieldsHigh correlation
engine_size is highly overall correlated with bore and 9 other fieldsHigh correlation
height is highly overall correlated with length and 2 other fieldsHigh correlation
highway_mpg is highly overall correlated with bore and 9 other fieldsHigh correlation
horsepower is highly overall correlated with bore and 9 other fieldsHigh correlation
length is highly overall correlated with bore and 9 other fieldsHigh correlation
num_of_cylinders is highly overall correlated with city_mpg and 5 other fieldsHigh correlation
price is highly overall correlated with bore and 9 other fieldsHigh correlation
symboling is highly overall correlated with height and 1 other fieldsHigh correlation
wheel_base is highly overall correlated with bore and 9 other fieldsHigh correlation
width is highly overall correlated with bore and 8 other fieldsHigh correlation
symboling has 67 (32.7%) zerosZeros

Reproduction

Analysis started2023-11-27 13:01:58.979961
Analysis finished2023-11-27 13:02:37.648065
Duration38.67 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

symboling
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83414634
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Negative25
Negative (%)12.2%
Memory size1.7 KiB
2023-11-27T18:32:37.747433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2453068
Coefficient of variation (CV)1.4929117
Kurtosis-0.67627136
Mean0.83414634
Median Absolute Deviation (MAD)1
Skewness0.21107227
Sum171
Variance1.5507891
MonotonicityNot monotonic
2023-11-27T18:32:37.914299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
-1 22
 
10.7%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.7%
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
ValueCountFrequency (%)
3 27
13.2%
2 32
15.6%
1 54
26.3%
0 67
32.7%
-1 22
 
10.7%
-2 3
 
1.5%

wheel_base
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.756585
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:38.097587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.02
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0217757
Coefficient of variation (CV)0.060975941
Kurtosis1.0170389
Mean98.756585
Median Absolute Deviation (MAD)2.7
Skewness1.0502138
Sum20245.1
Variance36.261782
MonotonicityNot monotonic
2023-11-27T18:32:38.324402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.5 21
 
10.2%
93.7 20
 
9.8%
95.7 13
 
6.3%
96.5 8
 
3.9%
97.3 7
 
3.4%
98.4 7
 
3.4%
104.3 6
 
2.9%
100.4 6
 
2.9%
107.9 6
 
2.9%
98.8 6
 
2.9%
Other values (43) 105
51.2%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.4%
93.3 1
 
0.5%
93.7 20
9.8%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.4%
108 1
 
0.5%
107.9 6
2.9%
106.7 1
 
0.5%

length
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.04927
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:38.547414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.14
Q1166.3
median173.2
Q3183.1
95-th percentile196.36
Maximum208.1
Range67
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation12.337289
Coefficient of variation (CV)0.070883886
Kurtosis-0.082894853
Mean174.04927
Median Absolute Deviation (MAD)6.9
Skewness0.15595377
Sum35680.1
Variance152.20869
MonotonicityNot monotonic
2023-11-27T18:32:38.768538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.3%
188.8 11
 
5.4%
171.7 7
 
3.4%
186.7 7
 
3.4%
166.3 7
 
3.4%
165.3 6
 
2.9%
177.8 6
 
2.9%
176.2 6
 
2.9%
186.6 6
 
2.9%
172 5
 
2.4%
Other values (65) 129
62.9%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 3
 
1.5%
156.9 1
 
0.5%
157.1 1
 
0.5%
157.3 15
7.3%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.0%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

width
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.907805
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:39.014505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.9
95-th percentile70.46
Maximum72.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1452039
Coefficient of variation (CV)0.032548556
Kurtosis0.70276424
Mean65.907805
Median Absolute Deviation (MAD)1.4
Skewness0.9040035
Sum13511.1
Variance4.6018996
MonotonicityNot monotonic
2023-11-27T18:32:39.230818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
63.8 24
 
11.7%
66.5 23
 
11.2%
65.4 15
 
7.3%
63.6 11
 
5.4%
64.4 10
 
4.9%
68.4 10
 
4.9%
64 9
 
4.4%
65.5 8
 
3.9%
65.2 7
 
3.4%
64.2 6
 
2.9%
Other values (34) 82
40.0%
ValueCountFrequency (%)
60.3 1
 
0.5%
61.8 1
 
0.5%
62.5 1
 
0.5%
63.4 1
 
0.5%
63.6 11
5.4%
63.8 24
11.7%
63.9 3
 
1.5%
64 9
 
4.4%
64.1 2
 
1.0%
64.2 6
 
2.9%
ValueCountFrequency (%)
72.3 1
 
0.5%
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.0%

height
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.724878
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:39.430875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.443522
Coefficient of variation (CV)0.045482132
Kurtosis-0.44381237
Mean53.724878
Median Absolute Deviation (MAD)1.6
Skewness0.063122732
Sum11013.6
Variance5.9707996
MonotonicityNot monotonic
2023-11-27T18:32:40.108929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
6.8%
52 12
 
5.9%
55.7 12
 
5.9%
54.1 10
 
4.9%
54.5 10
 
4.9%
55.5 9
 
4.4%
56.7 8
 
3.9%
54.3 8
 
3.9%
52.6 7
 
3.4%
56.1 7
 
3.4%
Other values (39) 108
52.7%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
2.9%
50.5 2
 
1.0%
50.6 5
 
2.4%
50.8 14
6.8%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
3.9%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.4%

curb_weight
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.5659
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:40.398249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1901
Q12145
median2414
Q32935
95-th percentile3503
Maximum4066
Range2578
Interquartile range (IQR)790

Descriptive statistics

Standard deviation520.6802
Coefficient of variation (CV)0.20374361
Kurtosis-0.042853766
Mean2555.5659
Median Absolute Deviation (MAD)386
Skewness0.68139819
Sum523891
Variance271107.87
MonotonicityNot monotonic
2023-11-27T18:32:40.614342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1918 3
 
1.5%
2275 3
 
1.5%
1989 3
 
1.5%
2410 2
 
1.0%
2191 2
 
1.0%
2535 2
 
1.0%
2024 2
 
1.0%
2414 2
 
1.0%
4066 2
 
1.0%
Other values (161) 180
87.8%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 2
1.0%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

num_of_cylinders
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3804878
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:40.795389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median4
Q34
95-th percentile6
Maximum12
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0808538
Coefficient of variation (CV)0.24674279
Kurtosis13.714866
Mean4.3804878
Median Absolute Deviation (MAD)0
Skewness2.817459
Sum898
Variance1.1682449
MonotonicityNot monotonic
2023-11-27T18:32:40.957433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 159
77.6%
6 24
 
11.7%
5 11
 
5.4%
8 5
 
2.4%
2 4
 
2.0%
3 1
 
0.5%
12 1
 
0.5%
ValueCountFrequency (%)
2 4
 
2.0%
3 1
 
0.5%
4 159
77.6%
5 11
 
5.4%
6 24
 
11.7%
8 5
 
2.4%
12 1
 
0.5%
ValueCountFrequency (%)
12 1
 
0.5%
8 5
 
2.4%
6 24
 
11.7%
5 11
 
5.4%
4 159
77.6%
3 1
 
0.5%
2 4
 
2.0%

engine_size
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90732
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:41.131010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median120
Q3141
95-th percentile201.2
Maximum326
Range265
Interquartile range (IQR)44

Descriptive statistics

Standard deviation41.642693
Coefficient of variation (CV)0.32813469
Kurtosis5.3056821
Mean126.90732
Median Absolute Deviation (MAD)23
Skewness1.947655
Sum26016
Variance1734.1139
MonotonicityNot monotonic
2023-11-27T18:32:41.331012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
122 15
 
7.3%
92 15
 
7.3%
97 14
 
6.8%
98 14
 
6.8%
108 13
 
6.3%
90 12
 
5.9%
110 12
 
5.9%
109 8
 
3.9%
120 7
 
3.4%
141 7
 
3.4%
Other values (34) 88
42.9%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
5.9%
91 5
 
2.4%
92 15
7.3%
97 14
6.8%
98 14
6.8%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.0%
181 6
2.9%

bore
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3293659
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:41.548121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.27085755
Coefficient of variation (CV)0.081354096
Kurtosis-0.78537193
Mean3.3293659
Median Absolute Deviation (MAD)0.26
Skewness0.024510547
Sum682.52
Variance0.073363812
MonotonicityNot monotonic
2023-11-27T18:32:41.747665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.62 23
 
11.2%
3.19 20
 
9.8%
3.15 15
 
7.3%
3.31 12
 
5.9%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.78 8
 
3.9%
3.43 8
 
3.9%
3.27 7
 
3.4%
Other values (28) 79
38.5%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.4%
2.92 1
 
0.5%
2.97 12
5.9%
2.99 1
 
0.5%
3.01 5
2.4%
3.03 12
5.9%
3.05 6
2.9%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 2
 
1.0%
3.8 2
 
1.0%
3.78 8
 
3.9%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.4%
3.63 2
 
1.0%
3.62 23
11.2%
3.61 1
 
0.5%
3.6 1
 
0.5%

stroke
Real number (ℝ)

Distinct36
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2560976
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:41.964738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31363365
Coefficient of variation (CV)0.096321946
Kurtosis2.1781141
Mean3.2560976
Median Absolute Deviation (MAD)0.14
Skewness-0.6960331
Sum667.5
Variance0.098366069
MonotonicityNot monotonic
2023-11-27T18:32:42.147738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.4 20
 
9.8%
3.23 14
 
6.8%
3.15 14
 
6.8%
3.03 14
 
6.8%
3.29 13
 
6.3%
3.39 13
 
6.3%
2.64 11
 
5.4%
3.35 9
 
4.4%
3.46 8
 
3.9%
3.11 6
 
2.9%
Other values (26) 83
40.5%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.36 1
 
0.5%
2.64 11
5.4%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
6.8%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 3
 
1.5%
3.86 4
2.0%
3.64 5
2.4%
3.58 6
2.9%
3.54 4
2.0%
3.52 5
2.4%
3.5 6
2.9%
3.47 4
2.0%
3.46 8
3.9%

compression_ratio
Real number (ℝ)

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142537
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:42.308175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9720403
Coefficient of variation (CV)0.39162199
Kurtosis5.2330543
Mean10.142537
Median Absolute Deviation (MAD)0.4
Skewness2.6108625
Sum2079.22
Variance15.777104
MonotonicityNot monotonic
2023-11-27T18:32:42.490558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.4%
9.4 26
12.7%
8.5 14
 
6.8%
9.5 13
 
6.3%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.4%
Other values (22) 58
28.3%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.4%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.4%
8.5 14
6.8%
ValueCountFrequency (%)
23 5
2.4%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.4%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

horsepower
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.16585
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:42.674978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile180.8
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.529733
Coefficient of variation (CV)0.3794884
Kurtosis2.6851678
Mean104.16585
Median Absolute Deviation (MAD)25
Skewness1.403441
Sum21354
Variance1562.5998
MonotonicityNot monotonic
2023-11-27T18:32:42.890689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.3%
70 11
 
5.4%
69 10
 
4.9%
116 9
 
4.4%
95 9
 
4.4%
110 8
 
3.9%
114 6
 
2.9%
160 6
 
2.9%
101 6
 
2.9%
62 6
 
2.9%
Other values (49) 115
56.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
2.9%
64 1
 
0.5%
68 19
9.3%
69 10
4.9%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

peak_rpm
Real number (ℝ)

Distinct23
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5126.0976
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:43.067574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5980
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation477.03577
Coefficient of variation (CV)0.093060221
Kurtosis0.084844573
Mean5126.0976
Median Absolute Deviation (MAD)300
Skewness0.068978859
Sum1050850
Variance227563.13
MonotonicityNot monotonic
2023-11-27T18:32:43.247541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5500 37
18.0%
4800 36
17.6%
5000 27
13.2%
5200 25
12.2%
5400 13
 
6.3%
6000 9
 
4.4%
5800 7
 
3.4%
5250 7
 
3.4%
4500 7
 
3.4%
4150 5
 
2.4%
Other values (13) 32
15.6%
ValueCountFrequency (%)
4150 5
 
2.4%
4200 5
 
2.4%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 36
17.6%
4900 1
 
0.5%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.0%
5400 13
 
6.3%
5300 1
 
0.5%
5250 7
 
3.4%

city_mpg
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.219512
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:43.431043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5421417
Coefficient of variation (CV)0.25940794
Kurtosis0.57864834
Mean25.219512
Median Absolute Deviation (MAD)5
Skewness0.66370403
Sum5170
Variance42.799617
MonotonicityNot monotonic
2023-11-27T18:32:43.664967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
13.7%
19 27
13.2%
24 22
10.7%
27 14
 
6.8%
17 13
 
6.3%
26 12
 
5.9%
23 12
 
5.9%
21 8
 
3.9%
25 8
 
3.9%
30 8
 
3.9%
Other values (19) 53
25.9%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 6
 
2.9%
17 13
6.3%
18 3
 
1.5%
19 27
13.2%
20 3
 
1.5%
21 8
 
3.9%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 7
3.4%
37 6
2.9%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

highway_mpg
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.75122
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:43.873889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42.8
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8864431
Coefficient of variation (CV)0.22394049
Kurtosis0.44007038
Mean30.75122
Median Absolute Deviation (MAD)5
Skewness0.53999719
Sum6304
Variance47.423099
MonotonicityNot monotonic
2023-11-27T18:32:44.064277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.3%
38 17
 
8.3%
24 17
 
8.3%
30 16
 
7.8%
32 16
 
7.8%
34 14
 
6.8%
37 13
 
6.3%
28 13
 
6.3%
29 10
 
4.9%
33 9
 
4.4%
Other values (20) 61
29.8%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 8
3.9%
23 7
 
3.4%
24 17
8.3%
25 19
9.3%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 4
 
2.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.3%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct186
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13150.307
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-11-27T18:32:44.281521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6197
Q17788
median10295
Q316500
95-th percentile32472.4
Maximum45400
Range40282
Interquartile range (IQR)8712

Descriptive statistics

Standard deviation7879.1213
Coefficient of variation (CV)0.59915872
Kurtosis3.3748636
Mean13150.307
Median Absolute Deviation (MAD)3204
Skewness1.8409793
Sum2695813
Variance62080553
MonotonicityNot monotonic
2023-11-27T18:32:44.491374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10295 5
 
2.4%
8921 2
 
1.0%
18150 2
 
1.0%
8845 2
 
1.0%
8495 2
 
1.0%
7775 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
7957 2
 
1.0%
Other values (176) 182
88.8%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

Interactions

2023-11-27T18:32:34.864479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:31:59.379350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:01.732144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:03.993284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:06.340529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:08.489505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:10.933830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:13.064700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:15.472519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:17.864227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:20.234302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:22.702167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:25.102117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:27.648654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:30.024266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:32.283940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:35.002417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:31:59.532335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:01.859925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:04.136562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:06.461357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:08.622669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:11.064522image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:13.181363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:15.610417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:18.038603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:20.350405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:22.841776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:25.231080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:27.815633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:30.148772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:32.421430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:35.164305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:31:59.697311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:02.020846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:04.275321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:06.600255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:08.748064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:11.197754image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:13.314348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:15.772149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:18.200355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:20.481045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:22.986948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:25.382892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:27.985740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:30.292975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:32.642968image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:35.322458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:31:59.856724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:02.157122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:04.414063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:06.735260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:09.177122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:11.343179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:13.454938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:15.929899image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:18.367514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:20.597522image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:23.130331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:25.518630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:28.185419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:30.445454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:32.907048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:35.450654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:00.034521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:02.305616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:04.549077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:06.850106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:09.299012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:11.476443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:13.584060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:16.082043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:18.514235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:20.732011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:23.258188image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:25.647663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:28.332016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:30.592691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:33.099575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:35.591683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:00.176098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:02.433189image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:04.674843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:06.975571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:09.423272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:11.601565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:13.714352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:16.215028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:18.647799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:20.862549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:23.385694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:25.777530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:28.473925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:30.730877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:33.283836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:35.714239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:00.329779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:02.574072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:04.809857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:07.110178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:09.551358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:11.727014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:13.874925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:16.372571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:18.780875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:21.000239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:23.522775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:25.899057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:28.596429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:30.847542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:33.447810image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:35.847506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:00.449440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:02.718724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:04.937861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:07.255675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:09.675887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:11.851656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:14.016842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:16.516815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:18.932627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:21.126372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:23.664796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:26.014700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:28.727094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:30.987100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:33.618940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:35.984544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:00.591173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:02.858752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:05.088291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:07.376948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:09.796049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:11.980077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:14.183435image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:16.656092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:19.080045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:21.263275image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:23.830036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:26.154989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:28.872373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:31.122047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:33.764307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:36.114426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:00.725907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:02.993594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:05.238478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:07.534205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:09.968281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:12.115291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:14.383137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:16.804752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:19.232784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:21.407381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:24.031182image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:26.311710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:29.031707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:31.247897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:33.915144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:36.264355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:00.865834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:03.139204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:05.382716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:07.670645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:10.121497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:12.231070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:14.524616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:16.932732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:19.377288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:21.532504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:24.240014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:26.473661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:29.182986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:31.364244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:34.047442image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:36.419019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:01.030667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:03.286813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:05.525336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:07.815262image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:10.264358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:12.364367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:14.658764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:17.132823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:19.546062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:21.671758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:24.398514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:26.677858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:29.322436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:31.514698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:34.188297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:36.557216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:01.169177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:03.407929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:05.652726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:07.925493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:10.402955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:12.501775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:14.801915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:17.296554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:19.681287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:21.800125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:24.532935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:26.867781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:29.447762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:31.634689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:34.318406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:36.679887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:01.276171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:03.566324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:05.783269image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:08.044352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:10.514462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:12.630996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:14.963440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:17.431267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:19.801564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:21.925148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:24.651391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:27.061223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:29.580841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:31.755010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:34.440344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:36.822377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:01.412244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:03.706912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:05.950462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:08.189637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:10.648520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:12.781300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:15.172698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:17.582280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:19.945633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:22.072075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:24.797386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:27.251297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:29.731289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:31.968482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:34.585698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:36.964478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:01.584110image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:03.865882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:06.171411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:08.331251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:10.798043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:12.929908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:15.329812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:17.722955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:20.081002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:22.220197image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:24.947834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:27.467612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:29.885604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:32.131662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T18:32:34.727265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-27T18:32:44.697979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
borecity_mpgcompression_ratiocurb_weightengine_sizeheighthighway_mpghorsepowerlengthnum_of_cylinderspeak_rpmpricestrokesymbolingwheel_basewidth
bore1.000-0.606-0.1630.7020.7050.219-0.6120.6390.6400.216-0.3020.636-0.081-0.1740.5390.610
city_mpg-0.6061.0000.479-0.813-0.730-0.0690.968-0.912-0.670-0.514-0.132-0.811-0.035-0.018-0.493-0.688
compression_ratio-0.1630.4791.000-0.219-0.2350.0000.445-0.354-0.193-0.136-0.024-0.169-0.0680.023-0.126-0.146
curb_weight0.702-0.813-0.2191.0000.8780.346-0.8340.8070.8900.569-0.2360.8950.162-0.2560.7650.864
engine_size0.705-0.730-0.2350.8781.0000.200-0.7210.8170.7830.692-0.2720.8110.285-0.1770.6480.771
height0.219-0.0690.0000.3460.2001.000-0.1330.0090.5250.092-0.2970.257-0.023-0.5230.6330.350
highway_mpg-0.6120.9680.445-0.834-0.721-0.1331.000-0.886-0.698-0.509-0.057-0.813-0.0340.053-0.539-0.701
horsepower0.639-0.912-0.3540.8070.8170.009-0.8861.0000.6610.5760.1140.8370.133-0.0080.5030.689
length0.640-0.670-0.1930.8900.7830.525-0.6980.6611.0000.466-0.2680.7990.186-0.3960.9120.888
num_of_cylinders0.216-0.514-0.1360.5690.6920.092-0.5090.5760.4661.000-0.0950.5610.057-0.1430.3650.467
peak_rpm-0.302-0.132-0.024-0.236-0.272-0.297-0.0570.114-0.268-0.0951.000-0.080-0.0650.283-0.312-0.197
price0.636-0.811-0.1690.8950.8110.257-0.8130.8370.7990.561-0.0801.0000.111-0.1430.6790.795
stroke-0.081-0.035-0.0680.1620.285-0.023-0.0340.1330.1860.057-0.0650.1111.000-0.0140.2260.241
symboling-0.174-0.0180.023-0.256-0.177-0.5230.053-0.008-0.396-0.1430.283-0.143-0.0141.000-0.538-0.254
wheel_base0.539-0.493-0.1260.7650.6480.633-0.5390.5030.9120.365-0.3120.6790.226-0.5381.0000.812
width0.610-0.688-0.1460.8640.7710.350-0.7010.6890.8880.467-0.1970.7950.241-0.2540.8121.000

Missing values

2023-11-27T18:32:37.182344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-27T18:32:37.523638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

symbolingwheel_baselengthwidthheightcurb_weightnum_of_cylindersengine_sizeborestrokecompression_ratiohorsepowerpeak_rpmcity_mpghighway_mpgprice
0388.6168.864.148.8254841303.472.689.0111.05000.0212713495.0
1388.6168.864.148.8254841303.472.689.0111.05000.0212716500.0
2194.5171.265.552.4282361522.683.479.0154.05000.0192616500.0
3299.8176.666.254.3233741093.193.4010.0102.05500.0243013950.0
4299.4176.666.454.3282451363.193.408.0115.05500.0182217450.0
5299.8177.366.353.1250751363.193.408.5110.05500.0192515250.0
61105.8192.771.455.7284451363.193.408.5110.05500.0192517710.0
71105.8192.771.455.7295451363.193.408.5110.05500.0192518920.0
81105.8192.771.455.9308651313.133.408.3140.05500.0172023875.0
9099.5178.267.952.0305351313.133.407.0160.05500.0162210295.0
symbolingwheel_baselengthwidthheightcurb_weightnum_of_cylindersengine_sizeborestrokecompression_ratiohorsepowerpeak_rpmcity_mpghighway_mpgprice
195-1104.3188.867.257.5303441413.783.159.5114.05400.0232813415.0
196-2104.3188.867.256.2293541413.783.159.5114.05400.0242815985.0
197-1104.3188.867.257.5304241413.783.159.5114.05400.0242816515.0
198-2104.3188.867.256.2304541303.623.157.5162.05100.0172218420.0
199-1104.3188.867.257.5315741303.623.157.5162.05100.0172218950.0
200-1109.1188.868.955.5295241413.783.159.5114.05400.0232816845.0
201-1109.1188.868.855.5304941413.783.158.7160.05300.0192519045.0
202-1109.1188.868.955.5301261733.582.878.8134.05500.0182321485.0
203-1109.1188.868.955.5321761453.013.4023.0106.04800.0262722470.0
204-1109.1188.868.955.5306241413.783.159.5114.05400.0192522625.0

Duplicate rows

Most frequently occurring

symbolingwheel_baselengthwidthheightcurb_weightnum_of_cylindersengine_sizeborestrokecompression_ratiohorsepowerpeak_rpmcity_mpghighway_mpgprice# duplicates
0193.7157.363.850.619674902.973.239.468.05500.031386229.02
1193.7157.363.850.821284983.033.397.6102.05500.024307957.02